Projects per year
Abstract
This paper discusses the challenges faced by the empirical macroeconomist and methods for surmounting them. These challenges arise due to the fact that macroeconometric models potentially include a large number of variables and allow for time variation in parameters. These considerations lead to models which have a large number of parameters to estimate relative to the number of observations. A wide range of approaches are surveyed which aim to overcome the resulting problems. We stress the related themes of prior shrinkage, model averaging and model selection. Subsequently, we consider a particular modelling approach in detail. This involves the use of dynamic model selection methods with large TVP-VARs. A forecasting exercise involving a large US macroeconomic data set
illustrates the practicality and empirical success of our approach.
illustrates the practicality and empirical success of our approach.
Original language | English |
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Number of pages | 34 |
Journal | Central European Journal of Economic Modelling and Econometrics |
Publication status | Accepted/In press - 2012 |
Keywords
- bayesian VAR
- state-space model
- forecasting
- time-varying coefficients
- TVP-VARs
- macroeconomic variables
- VARs
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Dive into the research topics of 'Using VARs and TVP-VARs with many macroeconomic variables'. Together they form a unique fingerprint.Projects
- 1 Finished
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Macroeconomic Forecasting in Turbulent Times
ESRC (Economic and Social Research Council)
1/10/10 → 30/09/13
Project: Research